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Module: tf.compat.v1.train

Support for training models.

See the Training guide.


experimental module: Public API for tf.train.experimental namespace.

queue_runner module: Public API for tf.train.queue_runner namespace.


class AdadeltaOptimizer: Optimizer that implements the Adadelta algorithm.

class AdagradDAOptimizer: Adagrad Dual Averaging algorithm for sparse linear models.

class AdagradOptimizer: Optimizer that implements the Adagrad algorithm.

class AdamOptimizer: Optimizer that implements the Adam algorithm.

class BytesList: Container that holds repeated fundamental values of byte type in the tf.train.Feature message.

class Checkpoint: Groups trackable objects, saving and restoring them.

class CheckpointManager: Manages multiple checkpoints by keeping some and deleting unneeded ones.

class CheckpointOptions: Options for constructing a Checkpoint.

class CheckpointSaverHook: Saves checkpoints every N steps or seconds.

class CheckpointSaverListener: Interface for listeners that take action before or after checkpoint save.

class ChiefSessionCreator: Creates a tf.compat.v1.Session for a chief.

class ClusterDef: A ProtocolMessage

class ClusterSpec: Represents a cluster as a set of "tasks", organized into "jobs".

class Coordinator: A coordinator for threads.

class Example: An Example is a mostly-normalized data format for storing data for training and inference.

class ExponentialMovingAverage: Maintains moving averages of variables by employing an exponential decay.

class Feature: A Feature is a list which may hold zero or more values.

class FeatureList: Contains zero or more values of tf.train.Features.

class FeatureLists: Contains the mapping from name to tf.train.FeatureList.

class Features: Protocol message for describing the features of a tf.train.Example.

class FeedFnHook: Runs feed_fn and sets the feed_dict accordingly.

class FinalOpsHook: A hook which evaluates Tensors at the end of a session.

class FloatList: Container that holds repeated fundamental values of float type in the tf.train.Feature message.

class FtrlOptimizer: Optimizer that implements the FTRL algorithm.

class GlobalStepWaiterHook: Delays execution until global step reaches wait_until_step.

class GradientDescentOptimizer: Optimizer that implements the gradient descent algorithm.

class Int64List: Container that holds repeated fundamental value of int64 type in the tf.train.Feature message.

class JobDef: A ProtocolMessage

class LoggingTensorHook: Prints the given tensors every N local steps, every N seconds, or at end.

class LooperThread: A thread that runs code repeatedly, optionally on a timer.

class MomentumOptimizer: Optimizer that implements the Momentum algorithm.

class MonitoredSession: Session-like object that handles initialization, recovery and hooks.

class NanLossDuringTrainingError: Unspecified run-time error.

class NanTensorHook: Monitors the loss tensor and stops training if loss is NaN.

class Optimizer: Base class for optimizers.

class ProfilerHook: Captures CPU/GPU profiling information every N steps or seconds.

class ProximalAdagradOptimizer: Optimizer that implements the Proximal Adagrad algorithm.

class ProximalGradientDescentOptimizer: Optimizer that implements the proximal gradient descent algorithm.

class QueueRunner: Holds a list of enqueue operations for a queue, each to be run in a thread.

class RMSPropOptimizer: Optimizer that implements the RMSProp algorithm (Tielemans et al